PPT-Unsupervised Learning of Visual Sense Models for Polysemous

Author : tatyana-admore | Published Date : 2017-07-02

Kate Saenko Trevor Darrell Deepak Polysemy Ambiguity of an individual word or phrase that can be used in different contexts to express two or more meanings Eg Present

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Unsupervised Learning of Visual Sense Models for Polysemous: Transcript

Kate Saenko Trevor Darrell Deepak Polysemy Ambiguity of an individual word or phrase that can be used in different contexts to express two or more meanings Eg Present right now Present a gift. progress. explain. pace. visual. auditory. kinaesthetic. digital. Aims of session:. Remind of what . Learning Styles. . are, . quick. methods to . identify. and . develop. them. Lots of . simple. Temporal Commonality Discovery. Wen-Sheng . Chu. , . Feng. Zhou and Fernando De la Torre. Robotics Institute, Carnegie Mellon University. July 9, . 2013. 1. Unsupervised Commonality Discovery. in . Images. . Image by kirkh.deviantart.com. Aditya. . Khosla. and Joseph Lim. Today’s class. Part 1: Introduction to deep learning. What is deep learning?. Why deep learning?. Some common deep learning algorithms. Face Alignment . by Robust . Nonrigid. Mapping. Related Work. Supervised . Face Alignment . Active appearance models, T. . Cootes. et al. TPAMI’01.. Generalized shape regularization model, L. . Gu. Corpora and Statistical Methods. Lecture 6. Word sense disambiguation. Part 2. What are word senses?. Cognitive definition: . mental representation of meaning . used in psychological experiments. relies on introspection (notoriously deceptive). 1. Semi-Supervised Learning. Can we improve the quality of our learning by combining labeled and unlabeled data. Usually a lot more unlabeled data available than labeled. Assume a set . L. of labeled data and . CS539. Prof. Carolina Ruiz. Department of Computer Science . (CS). & Bioinformatics and Computational Biology (BCB) Program. & Data Science (DS) Program. WPI. Most figures and images in this presentation were obtained from Google Images. General Classification Concepts. Unsupervised Classifications. Learning Objectives. What is image classification. ?. W. hat are the three broad classification strategies?. What are the general steps required to classify images? . Walker Wieland. GEOG 342. Introduction. Isocluster. Unsupervised. Interactive Supervised . Raster Analysis. Conclusions. Outline. GIS work, watershed analysis. Characterize amounts of impervious cover (IC) at spatial extents . ShaSha. . Xie. * Lei Chen. Microsoft ETS. 6/13/2013. Model Adaptation, Key to ASR Success. http://youtu.be/5FFRoYhTJQQ. Adaptation. Modern ASR systems are statistics-rich. Acoustic model (AM) uses GMM or DNN. Unsupervised Part-of-Speech Tagging with Bilingual Graph-Based Projections June 21 ACL 2011 Slav Petrov Google Research Dipanjan Das Carnegie Mellon University Part-of-Speech Tagging Portland has a thriving music scene . The Desired Brand Effect Stand Out in a Saturated Market with a Timeless Brand FROM BIG DATA. Richard Holaj. Humor GENERATING . introduction. very hard . problem. . deep. . semantic. . understanding. . cultural. . contextual. . clues. . solutions. . using. . labelling.

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